[A-AS01] 気象の予測可能性から制御可能性へ

[AAS01-P01] Improving numerical weather prediction for heavy rainfall with the assimilation of dual Multi-Parameter Phased Array Weather Radar

*James David Taylor^1,2、Shigenori Otsuka^1,3,2、Arata Amemiya^1,2、Shinsuke Satoh⁴、Takemasa Miyoshi^1,2,3,4,5,6 (1.RIKEN Research center for computational science、2.RIKEN Cluster for Pioneering Research、3.RIKEN Interdisciplinary Theorectical and Mathematical Sciences Program、4.National Institute for Information and Communciations Technology、5.University of Maryland、6.Japan Agency for Marine Earth Science and Technology)

キーワード：numerical weather prediction, phased array weather radar, data assimilation

Heavy, localized rainfall from convective weather systems can develop very rapidly in summertime, bringing the risk of flash flooding that can pose a severe threat to life and property. One of the most useful instruments to observe such weather systems is the multi-parameter phased array weather radars (MP-PAWR), a recently developed advanced X-band radar system designed to provide high-density observations of Doppler wind velocity and reflectivity. Through data assimilation within regional-scale numerical weather prediction (NWP) systems, these observations have provided a positive impact to both weather analyses and forecasts. Since the development of the Suita and Kobe MP-PAWR in 2012 and 2014 respectively, there has existed a common observation region, providing dual sets of MP-PAWR observations, thus providing the opportunity to perform dual radar assimilation and with it the potential for further improvement in short-range rain forecasts.
In this study we perform data assimilation experiments to assimilate observations from both Suita and Kobe MP-PAWR for the purpose of improving very short range forecasts of heavy rainfall. We use the SCALE-LETKF NWP modelling system with 1000-member ensemble for a 500m domain refreshed every 30-seconds with dual radar observations. Results showed improvements in the distribution and intensity of rainfall in both the analyses and forecasts up to 30-minute lead times compared to the assimilation of a single MP-PAWR dataset. We also showcase the progress to demonstrate real-time dual MP-PAWR assimilation within the SCALE-LETKF system at the Osaka World Expo 2025.